Autonomous driving has quickly become an area of interest for vehicle manufactures and navigation and mapping service providers. One particular area of interest is the use of computer vision to enable mapping and sensing of a vehicle's environment to support autonomous or semi-autonomous operation. Advances in available computing power has enabled this mapping and sensing to approach or achieve real-time operation through, e.g., machine learning (e.g., neural networks). As a result, one application of vision techniques in autonomous driving is localization of the vehicle with respect to known reference marks such as lane markings and/or other visible environmental features. However, despite the noted advances in available computing power, service providers and manufacturers still face significant technical challenges to enable computer vision systems to efficiently recognize features such as lane markings during driving activities, particularly within distributed, multi-node systems, employed in advanced neural networks or other similar machine learning system.